Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations9879
Missing cells614
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 MiB
Average record size in memory760.5 B

Variable types

Numeric11
Categorical4
Text7
DateTime1

Alerts

AverageSpeed is highly overall correlated with CircuitIDHigh correlation
CircuitID is highly overall correlated with AverageSpeed and 1 other fieldsHigh correlation
ConstructorName is highly overall correlated with ConstructorNationalityHigh correlation
ConstructorNationality is highly overall correlated with ConstructorNameHigh correlation
Grid is highly overall correlated with Points and 1 other fieldsHigh correlation
Nationality is highly overall correlated with PermanentNumberHigh correlation
PermanentNumber is highly overall correlated with Nationality and 1 other fieldsHigh correlation
Points is highly overall correlated with Grid and 1 other fieldsHigh correlation
Position is highly overall correlated with Grid and 1 other fieldsHigh correlation
Round is highly overall correlated with CircuitIDHigh correlation
Season is highly overall correlated with PermanentNumberHigh correlation
Code has 614 (6.2%) missing valuesMissing
Points has 5641 (57.1%) zerosZeros
PermanentNumber has 3518 (35.6%) zerosZeros
Laps has 355 (3.6%) zerosZeros
FastestLapLap has 1819 (18.4%) zerosZeros
AverageSpeed has 1819 (18.4%) zerosZeros
Time_seconds has 5761 (58.3%) zerosZeros
FastestLapTime_seconds has 1819 (18.4%) zerosZeros

Reproduction

Analysis started2024-08-13 00:02:55.397883
Analysis finished2024-08-13 00:03:08.231696
Duration12.83 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Season
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.2261
Minimum2000
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:08.298518image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12006
median2012
Q32018
95-th percentile2023
Maximum2024
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.0447821
Coefficient of variation (CV)0.0035009892
Kurtosis-1.1461237
Mean2012.2261
Median Absolute Deviation (MAD)6
Skewness-0.064265382
Sum19878782
Variance49.628954
MonotonicityIncreasing
2024-08-12T21:03:08.412489image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2012 480
 
4.9%
2016 462
 
4.7%
2010 456
 
4.6%
2011 456
 
4.6%
2023 440
 
4.5%
2022 440
 
4.5%
2021 440
 
4.5%
2018 420
 
4.3%
2019 420
 
4.3%
2013 418
 
4.2%
Other values (15) 5447
55.1%
ValueCountFrequency (%)
2000 373
3.8%
2001 374
3.8%
2002 362
3.7%
2003 320
3.2%
2004 360
3.6%
2005 376
3.8%
2006 396
4.0%
2007 374
3.8%
2008 368
3.7%
2009 340
3.4%
ValueCountFrequency (%)
2024 279
2.8%
2023 440
4.5%
2022 440
4.5%
2021 440
4.5%
2020 340
3.4%
2019 420
4.3%
2018 420
4.3%
2017 400
4.0%
2016 462
4.7%
2015 378
3.8%

Round
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9654823
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:08.519066image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum22
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.5804524
Coefficient of variation (CV)0.55997815
Kurtosis-1.0649226
Mean9.9654823
Median Absolute Deviation (MAD)5
Skewness0.10585767
Sum98449
Variance31.141449
MonotonicityNot monotonic
2024-08-12T21:03:08.622491image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4 530
 
5.4%
2 530
 
5.4%
3 529
 
5.4%
1 528
 
5.3%
11 528
 
5.3%
7 528
 
5.3%
8 528
 
5.3%
9 528
 
5.3%
10 527
 
5.3%
12 527
 
5.3%
Other values (12) 4596
46.5%
ValueCountFrequency (%)
1 528
5.3%
2 530
5.4%
3 529
5.4%
4 530
5.4%
5 526
5.3%
6 525
5.3%
7 528
5.3%
8 528
5.3%
9 528
5.3%
10 527
5.3%
ValueCountFrequency (%)
22 60
 
0.6%
21 122
 
1.2%
20 166
 
1.7%
19 296
3.0%
18 356
3.6%
17 482
4.9%
16 505
5.1%
15 506
5.1%
14 526
5.3%
13 526
5.3%

CircuitID
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size554.1 KiB
silverstone
 
547
catalunya
 
528
hungaroring
 
526
monza
 
506
monaco
 
506
Other values (33)
7266 

Length

Max length14
Median length12
Mean length8.4233222
Min length3

Characters and Unicode

Total characters83214
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowalbert_park
2nd rowalbert_park
3rd rowalbert_park
4th rowalbert_park
5th rowalbert_park

Common Values

ValueCountFrequency (%)
silverstone 547
 
5.5%
catalunya 528
 
5.3%
hungaroring 526
 
5.3%
monza 506
 
5.1%
monaco 506
 
5.1%
albert_park 487
 
4.9%
spa 484
 
4.9%
interlagos 484
 
4.9%
villeneuve 468
 
4.7%
suzuka 444
 
4.5%
Other values (28) 4899
49.6%

Length

2024-08-12T21:03:08.734374image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
silverstone 547
 
5.5%
catalunya 528
 
5.3%
hungaroring 526
 
5.3%
monza 506
 
5.1%
monaco 506
 
5.1%
albert_park 487
 
4.9%
spa 484
 
4.9%
interlagos 484
 
4.9%
villeneuve 468
 
4.7%
suzuka 444
 
4.5%
Other values (28) 4899
49.6%

Most occurring characters

ValueCountFrequency (%)
a 11582
13.9%
n 8339
 
10.0%
r 6875
 
8.3%
i 6553
 
7.9%
e 5521
 
6.6%
o 4639
 
5.6%
s 4573
 
5.5%
l 4502
 
5.4%
u 4088
 
4.9%
g 3933
 
4.7%
Other values (14) 22609
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81223
97.6%
Connector Punctuation 1991
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11582
14.3%
n 8339
10.3%
r 6875
 
8.5%
i 6553
 
8.1%
e 5521
 
6.8%
o 4639
 
5.7%
s 4573
 
5.6%
l 4502
 
5.5%
u 4088
 
5.0%
g 3933
 
4.8%
Other values (13) 20618
25.4%
Connector Punctuation
ValueCountFrequency (%)
_ 1991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81223
97.6%
Common 1991
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11582
14.3%
n 8339
10.3%
r 6875
 
8.5%
i 6553
 
8.1%
e 5521
 
6.8%
o 4639
 
5.7%
s 4573
 
5.6%
l 4502
 
5.5%
u 4088
 
5.0%
g 3933
 
4.8%
Other values (13) 20618
25.4%
Common
ValueCountFrequency (%)
_ 1991
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11582
13.9%
n 8339
 
10.0%
r 6875
 
8.3%
i 6553
 
7.9%
e 5521
 
6.6%
o 4639
 
5.6%
s 4573
 
5.5%
l 4502
 
5.4%
u 4088
 
4.9%
g 3933
 
4.7%
Other values (14) 22609
27.2%

Position
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.081081
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:08.837745image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile21
Maximum24
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.1614905
Coefficient of variation (CV)0.55603695
Kurtosis-1.1269268
Mean11.081081
Median Absolute Deviation (MAD)5
Skewness0.05152827
Sum109470
Variance37.963966
MonotonicityNot monotonic
2024-08-12T21:03:08.946100image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 469
 
4.7%
11 469
 
4.7%
18 469
 
4.7%
17 469
 
4.7%
16 469
 
4.7%
15 469
 
4.7%
14 469
 
4.7%
2 469
 
4.7%
12 469
 
4.7%
13 469
 
4.7%
Other values (14) 5189
52.5%
ValueCountFrequency (%)
1 469
4.7%
2 469
4.7%
3 469
4.7%
4 469
4.7%
5 469
4.7%
6 469
4.7%
7 469
4.7%
8 469
4.7%
9 469
4.7%
10 469
4.7%
ValueCountFrequency (%)
24 58
 
0.6%
23 58
 
0.6%
22 195
2.0%
21 199
2.0%
20 463
4.7%
19 464
4.7%
18 469
4.7%
17 469
4.7%
16 469
4.7%
15 469
4.7%

Points
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6493572
Minimum0
Maximum50
Zeros5641
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:09.053024image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile18
Maximum50
Range50
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.113076
Coefficient of variation (CV)1.6751103
Kurtosis3.5194619
Mean3.6493572
Median Absolute Deviation (MAD)0
Skewness1.9649679
Sum36052
Variance37.369698
MonotonicityNot monotonic
2024-08-12T21:03:09.167028image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 5641
57.1%
2 467
 
4.7%
1 466
 
4.7%
4 465
 
4.7%
6 459
 
4.6%
10 458
 
4.6%
8 411
 
4.2%
12 284
 
2.9%
15 283
 
2.9%
18 270
 
2.7%
Other values (20) 675
 
6.8%
ValueCountFrequency (%)
0 5641
57.1%
0.5 2
 
< 0.1%
1 466
 
4.7%
1.5 1
 
< 0.1%
2 467
 
4.7%
2.5 1
 
< 0.1%
3 177
 
1.8%
4 465
 
4.7%
5 128
 
1.3%
6 459
 
4.6%
ValueCountFrequency (%)
50 1
 
< 0.1%
36 1
 
< 0.1%
30 1
 
< 0.1%
26 35
 
0.4%
25 258
2.6%
24 1
 
< 0.1%
20 1
 
< 0.1%
19 23
 
0.2%
18 270
2.7%
16 11
 
0.1%
Distinct124
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size546.5 KiB
2024-08-12T21:03:09.377382image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length18
Median length15
Mean length7.6299221
Min length3

Characters and Unicode

Total characters75376
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowmichael_schumacher
2nd rowbarrichello
3rd rowralf_schumacher
4th rowvilleneuve
5th rowfisichella
ValueCountFrequency (%)
alonso 394
 
4.0%
raikkonen 352
 
3.6%
hamilton 346
 
3.5%
button 309
 
3.1%
vettel 300
 
3.0%
perez 273
 
2.8%
massa 271
 
2.7%
ricciardo 253
 
2.6%
bottas 237
 
2.4%
hulkenberg 220
 
2.2%
Other values (114) 6924
70.1%
2024-08-12T21:03:09.709492image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 7405
 
9.8%
a 7240
 
9.6%
r 5892
 
7.8%
n 5684
 
7.5%
o 5649
 
7.5%
l 5418
 
7.2%
i 5202
 
6.9%
s 4982
 
6.6%
t 4096
 
5.4%
c 2868
 
3.8%
Other values (17) 20940
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74460
98.8%
Connector Punctuation 916
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7405
 
9.9%
a 7240
 
9.7%
r 5892
 
7.9%
n 5684
 
7.6%
o 5649
 
7.6%
l 5418
 
7.3%
i 5202
 
7.0%
s 4982
 
6.7%
t 4096
 
5.5%
c 2868
 
3.9%
Other values (16) 20024
26.9%
Connector Punctuation
ValueCountFrequency (%)
_ 916
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74460
98.8%
Common 916
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7405
 
9.9%
a 7240
 
9.7%
r 5892
 
7.9%
n 5684
 
7.6%
o 5649
 
7.6%
l 5418
 
7.3%
i 5202
 
7.0%
s 4982
 
6.7%
t 4096
 
5.5%
c 2868
 
3.9%
Other values (16) 20024
26.9%
Common
ValueCountFrequency (%)
_ 916
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7405
 
9.8%
a 7240
 
9.6%
r 5892
 
7.8%
n 5684
 
7.5%
o 5649
 
7.5%
l 5418
 
7.2%
i 5202
 
6.9%
s 4982
 
6.6%
t 4096
 
5.4%
c 2868
 
3.8%
Other values (17) 20940
27.8%

Code
Text

MISSING 

Distinct96
Distinct (%)1.0%
Missing614
Missing (%)6.2%
Memory size489.8 KiB
2024-08-12T21:03:10.228111image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters27795
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowMSC
2nd rowBAR
3rd rowSCH
4th rowVIL
5th rowFIS
ValueCountFrequency (%)
alo 394
 
4.3%
rai 352
 
3.8%
ham 346
 
3.7%
but 309
 
3.3%
vet 300
 
3.2%
per 273
 
2.9%
mas 271
 
2.9%
ver 257
 
2.8%
ric 253
 
2.7%
bot 237
 
2.6%
Other values (86) 6273
67.7%
2024-08-12T21:03:10.568972image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2982
 
10.7%
R 2789
 
10.0%
O 2204
 
7.9%
S 2122
 
7.6%
I 1862
 
6.7%
T 1751
 
6.3%
E 1741
 
6.3%
U 1577
 
5.7%
L 1558
 
5.6%
B 1410
 
5.1%
Other values (14) 7799
28.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27795
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2982
 
10.7%
R 2789
 
10.0%
O 2204
 
7.9%
S 2122
 
7.6%
I 1862
 
6.7%
T 1751
 
6.3%
E 1741
 
6.3%
U 1577
 
5.7%
L 1558
 
5.6%
B 1410
 
5.1%
Other values (14) 7799
28.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 27795
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2982
 
10.7%
R 2789
 
10.0%
O 2204
 
7.9%
S 2122
 
7.6%
I 1862
 
6.7%
T 1751
 
6.3%
E 1741
 
6.3%
U 1577
 
5.7%
L 1558
 
5.6%
B 1410
 
5.1%
Other values (14) 7799
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2982
 
10.7%
R 2789
 
10.0%
O 2204
 
7.9%
S 2122
 
7.6%
I 1862
 
6.7%
T 1751
 
6.3%
E 1741
 
6.3%
U 1577
 
5.7%
L 1558
 
5.6%
B 1410
 
5.1%
Other values (14) 7799
28.1%

PermanentNumber
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.997469
Minimum0
Maximum99
Zeros3518
Zeros (%)35.6%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:10.700620image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q322
95-th percentile77
Maximum99
Range99
Interquartile range (IQR)22

Descriptive statistics

Standard deviation23.49529
Coefficient of variation (CV)1.3822816
Kurtosis3.1790002
Mean16.997469
Median Absolute Deviation (MAD)8
Skewness1.9008101
Sum167918
Variance552.02864
MonotonicityNot monotonic
2024-08-12T21:03:10.838253image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 3518
35.6%
14 394
 
4.0%
22 389
 
3.9%
7 352
 
3.6%
44 346
 
3.5%
5 300
 
3.0%
11 273
 
2.8%
19 271
 
2.7%
6 267
 
2.7%
3 253
 
2.6%
Other values (37) 3516
35.6%
ValueCountFrequency (%)
0 3518
35.6%
2 77
 
0.8%
3 253
 
2.6%
4 153
 
1.5%
5 300
 
3.0%
6 267
 
2.7%
7 352
 
3.6%
8 181
 
1.8%
9 119
 
1.2%
10 220
 
2.2%
ValueCountFrequency (%)
99 190
1.9%
98 13
 
0.1%
94 39
 
0.4%
89 1
 
< 0.1%
88 111
1.1%
81 36
 
0.4%
77 237
2.4%
63 118
1.2%
55 198
2.0%
53 5
 
0.1%
Distinct113
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size535.4 KiB
2024-08-12T21:03:11.057574image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.9385565
Min length3

Characters and Unicode

Total characters58667
Distinct characters55
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowMichael
2nd rowRubens
3rd rowRalf
4th rowJacques
5th rowGiancarlo
ValueCountFrequency (%)
nico 426
 
4.3%
fernando 394
 
4.0%
kimi 352
 
3.6%
lewis 346
 
3.5%
felipe 311
 
3.1%
jenson 309
 
3.1%
sebastian 300
 
3.0%
sergio 273
 
2.8%
daniel 253
 
2.6%
valtteri 237
 
2.4%
Other values (103) 6678
67.6%
2024-08-12T21:03:11.385243image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6475
 
11.0%
i 6423
 
10.9%
e 5737
 
9.8%
n 5091
 
8.7%
o 3801
 
6.5%
r 3634
 
6.2%
s 2593
 
4.4%
l 2522
 
4.3%
t 1922
 
3.3%
c 1701
 
2.9%
Other values (45) 18768
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48550
82.8%
Uppercase Letter 9998
 
17.0%
Dash Punctuation 119
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6475
13.3%
i 6423
13.2%
e 5737
11.8%
n 5091
10.5%
o 3801
7.8%
r 3634
7.5%
s 2593
 
5.3%
l 2522
 
5.2%
t 1922
 
4.0%
c 1701
 
3.5%
Other values (21) 8651
17.8%
Uppercase Letter
ValueCountFrequency (%)
J 1027
 
10.3%
M 861
 
8.6%
N 785
 
7.9%
S 767
 
7.7%
F 712
 
7.1%
R 698
 
7.0%
L 698
 
7.0%
K 653
 
6.5%
D 522
 
5.2%
C 501
 
5.0%
Other values (13) 2774
27.7%
Dash Punctuation
ValueCountFrequency (%)
- 119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58548
99.8%
Common 119
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6475
 
11.1%
i 6423
 
11.0%
e 5737
 
9.8%
n 5091
 
8.7%
o 3801
 
6.5%
r 3634
 
6.2%
s 2593
 
4.4%
l 2522
 
4.3%
t 1922
 
3.3%
c 1701
 
2.9%
Other values (44) 18649
31.9%
Common
ValueCountFrequency (%)
- 119
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58439
99.6%
None 228
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6475
 
11.1%
i 6423
 
11.0%
e 5737
 
9.8%
n 5091
 
8.7%
o 3801
 
6.5%
r 3634
 
6.2%
s 2593
 
4.4%
l 2522
 
4.3%
t 1922
 
3.3%
c 1701
 
2.9%
Other values (39) 18540
31.7%
None
ValueCountFrequency (%)
é 103
45.2%
É 58
25.4%
ô 40
 
17.5%
ó 21
 
9.2%
á 3
 
1.3%
Å¡ 3
 
1.3%
Distinct121
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size567.7 KiB
2024-08-12T21:03:11.632583image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length13
Median length10
Mean length7.2771536
Min length3

Characters and Unicode

Total characters71891
Distinct characters55
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowSchumacher
2nd rowBarrichello
3rd rowSchumacher
4th rowVilleneuve
5th rowFisichella
ValueCountFrequency (%)
alonso 394
 
3.8%
schumacher 355
 
3.4%
räikkönen 352
 
3.4%
hamilton 346
 
3.3%
button 309
 
3.0%
vettel 300
 
2.9%
pérez 273
 
2.6%
massa 271
 
2.6%
ricciardo 253
 
2.4%
verstappen 249
 
2.4%
Other values (119) 7237
70.0%
2024-08-12T21:03:11.981229image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6644
 
9.2%
a 5940
 
8.3%
n 5181
 
7.2%
o 5098
 
7.1%
l 4935
 
6.9%
i 4822
 
6.7%
r 4622
 
6.4%
s 3834
 
5.3%
t 3804
 
5.3%
c 2441
 
3.4%
Other values (45) 24570
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61364
85.4%
Uppercase Letter 10019
 
13.9%
Space Separator 460
 
0.6%
Other Punctuation 48
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6644
10.8%
a 5940
9.7%
n 5181
 
8.4%
o 5098
 
8.3%
l 4935
 
8.0%
i 4822
 
7.9%
r 4622
 
7.5%
s 3834
 
6.2%
t 3804
 
6.2%
c 2441
 
4.0%
Other values (19) 14043
22.9%
Uppercase Letter
ValueCountFrequency (%)
S 1113
11.1%
R 1084
10.8%
B 959
9.6%
H 838
8.4%
M 799
 
8.0%
V 760
 
7.6%
P 667
 
6.7%
A 636
 
6.3%
G 599
 
6.0%
K 503
 
5.0%
Other values (13) 2061
20.6%
Other Punctuation
ValueCountFrequency (%)
. 28
58.3%
' 20
41.7%
Space Separator
ValueCountFrequency (%)
460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71383
99.3%
Common 508
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6644
 
9.3%
a 5940
 
8.3%
n 5181
 
7.3%
o 5098
 
7.1%
l 4935
 
6.9%
i 4822
 
6.8%
r 4622
 
6.5%
s 3834
 
5.4%
t 3804
 
5.3%
c 2441
 
3.4%
Other values (42) 24062
33.7%
Common
ValueCountFrequency (%)
460
90.6%
. 28
 
5.5%
' 20
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70581
98.2%
None 1310
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6644
 
9.4%
a 5940
 
8.4%
n 5181
 
7.3%
o 5098
 
7.2%
l 4935
 
7.0%
i 4822
 
6.8%
r 4622
 
6.5%
s 3834
 
5.4%
t 3804
 
5.4%
c 2441
 
3.5%
Other values (41) 23260
33.0%
None
ValueCountFrequency (%)
ä 386
29.5%
ö 352
26.9%
é 352
26.9%
ü 220
16.8%
Distinct124
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
Minimum1964-06-11 00:00:00
Maximum2005-05-08 00:00:00
2024-08-12T21:03:12.108699image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:12.257135image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Nationality
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size541.8 KiB
German
1590 
British
1324 
Finnish
769 
Brazilian
766 
Spanish
762 
Other values (29)
4668 

Length

Max length13
Median length10
Mean length7.1424233
Min length4

Characters and Unicode

Total characters70560
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGerman
2nd rowBrazilian
3rd rowGerman
4th rowCanadian
5th rowItalian

Common Values

ValueCountFrequency (%)
German 1590
16.1%
British 1324
13.4%
Finnish 769
 
7.8%
Brazilian 766
 
7.8%
Spanish 762
 
7.7%
French 738
 
7.5%
Italian 561
 
5.7%
Australian 506
 
5.1%
Dutch 336
 
3.4%
Mexican 332
 
3.4%
Other values (24) 2195
22.2%

Length

2024-08-12T21:03:12.400679image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
german 1590
16.0%
british 1324
13.4%
finnish 769
 
7.8%
brazilian 766
 
7.7%
spanish 762
 
7.7%
french 738
 
7.4%
italian 561
 
5.7%
australian 506
 
5.1%
dutch 336
 
3.4%
mexican 332
 
3.4%
Other values (25) 2225
22.5%

Most occurring characters

ValueCountFrequency (%)
i 9461
13.4%
a 9189
13.0%
n 9127
12.9%
r 5210
 
7.4%
s 4956
 
7.0%
h 4478
 
6.3%
e 4406
 
6.2%
t 2881
 
4.1%
l 2231
 
3.2%
B 2152
 
3.0%
Other values (30) 16469
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60621
85.9%
Uppercase Letter 9909
 
14.0%
Space Separator 30
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9461
15.6%
a 9189
15.2%
n 9127
15.1%
r 5210
8.6%
s 4956
8.2%
h 4478
7.4%
e 4406
7.3%
t 2881
 
4.8%
l 2231
 
3.7%
m 1753
 
2.9%
Other values (12) 6929
11.4%
Uppercase Letter
ValueCountFrequency (%)
B 2152
21.7%
G 1590
16.0%
F 1507
15.2%
S 914
9.2%
A 691
 
7.0%
I 646
 
6.5%
D 519
 
5.2%
M 487
 
4.9%
C 475
 
4.8%
J 308
 
3.1%
Other values (7) 620
 
6.3%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70530
> 99.9%
Common 30
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9461
13.4%
a 9189
13.0%
n 9127
12.9%
r 5210
 
7.4%
s 4956
 
7.0%
h 4478
 
6.3%
e 4406
 
6.2%
t 2881
 
4.1%
l 2231
 
3.2%
B 2152
 
3.1%
Other values (29) 16439
23.3%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9461
13.4%
a 9189
13.0%
n 9127
12.9%
r 5210
 
7.4%
s 4956
 
7.0%
h 4478
 
6.3%
e 4406
 
6.2%
t 2881
 
4.1%
l 2231
 
3.2%
B 2152
 
3.0%
Other values (30) 16469
23.3%

ConstructorName
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size549.4 KiB
Ferrari
938 
McLaren
938 
Williams
937 
Red Bull
768 
Sauber
590 
Other values (33)
5708 

Length

Max length14
Median length12
Mean length7.9355198
Min length3

Characters and Unicode

Total characters78395
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFerrari
2nd rowFerrari
3rd rowWilliams
4th rowBAR
5th rowBenetton

Common Values

ValueCountFrequency (%)
Ferrari 938
 
9.5%
McLaren 938
 
9.5%
Williams 937
 
9.5%
Red Bull 768
 
7.8%
Sauber 590
 
6.0%
Mercedes 590
 
6.0%
Renault 556
 
5.6%
Toro Rosso 536
 
5.4%
Force India 424
 
4.3%
Haas F1 Team 360
 
3.6%
Other values (28) 3242
32.8%

Length

2024-08-12T21:03:12.508392image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ferrari 938
 
6.9%
mclaren 938
 
6.9%
williams 937
 
6.9%
red 768
 
5.6%
bull 768
 
5.6%
sauber 730
 
5.4%
f1 702
 
5.2%
mercedes 590
 
4.3%
renault 556
 
4.1%
team 548
 
4.0%
Other values (35) 6130
45.1%

Most occurring characters

ValueCountFrequency (%)
a 8417
 
10.7%
r 7878
 
10.0%
e 7408
 
9.4%
i 4703
 
6.0%
o 4629
 
5.9%
l 4500
 
5.7%
s 3882
 
5.0%
3726
 
4.8%
n 3394
 
4.3%
u 2963
 
3.8%
Other values (28) 26895
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58238
74.3%
Uppercase Letter 15693
 
20.0%
Space Separator 3726
 
4.8%
Decimal Number 738
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8417
14.5%
r 7878
13.5%
e 7408
12.7%
i 4703
8.1%
o 4629
7.9%
l 4500
7.7%
s 3882
6.7%
n 3394
5.8%
u 2963
 
5.1%
d 2302
 
4.0%
Other values (11) 8162
14.0%
Uppercase Letter
ValueCountFrequency (%)
R 2492
15.9%
M 2335
14.9%
F 2100
13.4%
T 1646
10.5%
B 1242
7.9%
L 1168
7.4%
W 1077
6.9%
A 1068
6.8%
S 850
 
5.4%
H 582
 
3.7%
Other values (5) 1133
7.2%
Space Separator
ValueCountFrequency (%)
3726
100.0%
Decimal Number
ValueCountFrequency (%)
1 738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73931
94.3%
Common 4464
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8417
 
11.4%
r 7878
 
10.7%
e 7408
 
10.0%
i 4703
 
6.4%
o 4629
 
6.3%
l 4500
 
6.1%
s 3882
 
5.3%
n 3394
 
4.6%
u 2963
 
4.0%
R 2492
 
3.4%
Other values (26) 23665
32.0%
Common
ValueCountFrequency (%)
3726
83.5%
1 738
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8417
 
10.7%
r 7878
 
10.0%
e 7408
 
9.4%
i 4703
 
6.0%
o 4629
 
5.9%
l 4500
 
5.7%
s 3882
 
5.0%
3726
 
4.8%
n 3394
 
4.3%
u 2963
 
3.8%
Other values (28) 26895
34.3%

ConstructorNationality
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size538.4 KiB
British
2919 
Italian
1942 
Swiss
798 
French
783 
Austrian
768 
Other values (9)
2669 

Length

Max length9
Median length7
Mean length6.7909707
Min length5

Characters and Unicode

Total characters67088
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowItalian
2nd rowItalian
3rd rowBritish
4th rowBritish
5th rowItalian

Common Values

ValueCountFrequency (%)
British 2919
29.5%
Italian 1942
19.7%
Swiss 798
 
8.1%
French 783
 
7.9%
Austrian 768
 
7.8%
German 730
 
7.4%
Japanese 464
 
4.7%
Indian 424
 
4.3%
American 360
 
3.6%
Irish 208
 
2.1%
Other values (4) 483
 
4.9%

Length

2024-08-12T21:03:12.627088image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
british 2919
29.5%
italian 1942
19.7%
swiss 798
 
8.1%
french 783
 
7.9%
austrian 768
 
7.8%
german 730
 
7.4%
japanese 464
 
4.7%
indian 424
 
4.3%
american 360
 
3.6%
irish 208
 
2.1%
Other values (4) 483
 
4.9%

Most occurring characters

ValueCountFrequency (%)
i 10779
16.1%
a 7911
11.8%
s 6533
9.7%
n 6336
9.4%
r 5768
8.6%
t 5671
8.5%
h 4068
 
6.1%
B 2919
 
4.4%
e 2801
 
4.2%
I 2574
 
3.8%
Other values (16) 11728
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57209
85.3%
Uppercase Letter 9879
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 10779
18.8%
a 7911
13.8%
s 6533
11.4%
n 6336
11.1%
r 5768
10.1%
t 5671
9.9%
h 4068
 
7.1%
e 2801
 
4.9%
l 2130
 
3.7%
c 1185
 
2.1%
Other values (6) 4027
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
B 2919
29.5%
I 2574
26.1%
A 1128
 
11.4%
S 914
 
9.3%
F 783
 
7.9%
G 730
 
7.4%
J 464
 
4.7%
M 188
 
1.9%
R 137
 
1.4%
D 42
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 67088
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 10779
16.1%
a 7911
11.8%
s 6533
9.7%
n 6336
9.4%
r 5768
8.6%
t 5671
8.5%
h 4068
 
6.1%
B 2919
 
4.4%
e 2801
 
4.2%
I 2574
 
3.8%
Other values (16) 11728
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 10779
16.1%
a 7911
11.8%
s 6533
9.7%
n 6336
9.4%
r 5768
8.6%
t 5671
8.5%
h 4068
 
6.1%
B 2919
 
4.4%
e 2801
 
4.2%
I 2574
 
3.8%
Other values (16) 11728
17.5%

Grid
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.916793
Minimum0
Maximum24
Zeros87
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:12.737826image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median11
Q316
95-th percentile21
Maximum24
Range24
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.1934758
Coefficient of variation (CV)0.56733471
Kurtosis-1.1149908
Mean10.916793
Median Absolute Deviation (MAD)5
Skewness0.054933939
Sum107847
Variance38.359142
MonotonicityNot monotonic
2024-08-12T21:03:12.984247image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
17 470
 
4.8%
3 469
 
4.7%
4 469
 
4.7%
9 469
 
4.7%
1 469
 
4.7%
6 469
 
4.7%
5 469
 
4.7%
12 468
 
4.7%
13 468
 
4.7%
7 468
 
4.7%
Other values (15) 5191
52.5%
ValueCountFrequency (%)
0 87
 
0.9%
1 469
4.7%
2 467
4.7%
3 469
4.7%
4 469
4.7%
5 469
4.7%
6 469
4.7%
7 468
4.7%
8 468
4.7%
9 469
4.7%
ValueCountFrequency (%)
24 54
 
0.5%
23 56
 
0.6%
22 190
1.9%
21 195
2.0%
20 418
4.2%
19 456
4.6%
18 464
4.7%
17 470
4.8%
16 467
4.7%
15 467
4.7%

Laps
Real number (ℝ)

ZEROS 

Distinct80
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.089078
Minimum0
Maximum87
Zeros355
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:13.103681image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q150
median56
Q366
95-th percentile72
Maximum87
Range87
Interquartile range (IQR)16

Descriptive statistics

Standard deviation19.258043
Coefficient of variation (CV)0.36971364
Kurtosis1.2331885
Mean52.089078
Median Absolute Deviation (MAD)9
Skewness-1.3524014
Sum514588
Variance370.8722
MonotonicityNot monotonic
2024-08-12T21:03:13.229431image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 670
 
6.8%
70 668
 
6.8%
53 609
 
6.2%
57 532
 
5.4%
52 518
 
5.2%
55 475
 
4.8%
71 406
 
4.1%
69 400
 
4.0%
58 367
 
3.7%
0 355
 
3.6%
Other values (70) 4879
49.4%
ValueCountFrequency (%)
0 355
3.6%
1 88
 
0.9%
2 48
 
0.5%
3 27
 
0.3%
4 22
 
0.2%
5 36
 
0.4%
6 35
 
0.4%
7 34
 
0.3%
8 38
 
0.4%
9 43
 
0.4%
ValueCountFrequency (%)
87 17
 
0.2%
78 173
 
1.8%
77 110
 
1.1%
76 63
 
0.6%
75 23
 
0.2%
74 6
 
0.1%
73 50
 
0.5%
72 88
 
0.9%
71 406
4.1%
70 668
6.8%

Status
Text

Distinct104
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size546.1 KiB
2024-08-12T21:03:13.398290image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length16
Median length8
Mean length7.5883187
Min length3

Characters and Unicode

Total characters74965
Distinct characters58
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.3%

Sample

1st rowFinished
2nd rowFinished
3rd rowFinished
4th rowFinished
5th rowFinished
ValueCountFrequency (%)
finished 4579
34.5%
lap 2218
16.7%
1 2218
16.7%
laps 780
 
5.9%
2 530
 
4.0%
collision 464
 
3.5%
engine 304
 
2.3%
accident 259
 
2.0%
gearbox 157
 
1.2%
3 143
 
1.1%
Other values (102) 1626
 
12.2%
2024-08-12T21:03:13.686517image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 11426
15.2%
s 6558
 
8.7%
n 6492
 
8.7%
e 6359
 
8.5%
d 5175
 
6.9%
h 4765
 
6.4%
F 4626
 
6.2%
a 3851
 
5.1%
3399
 
4.5%
p 3263
 
4.4%
Other values (48) 19051
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55621
74.2%
Uppercase Letter 9931
 
13.2%
Space Separator 3399
 
4.5%
Decimal Number 3014
 
4.0%
Math Symbol 2998
 
4.0%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 11426
20.5%
s 6558
11.8%
n 6492
11.7%
e 6359
11.4%
d 5175
9.3%
h 4765
8.6%
a 3851
 
6.9%
p 3263
 
5.9%
o 1478
 
2.7%
l 1466
 
2.6%
Other values (15) 4788
8.6%
Uppercase Letter
ValueCountFrequency (%)
F 4626
46.6%
L 2999
30.2%
C 487
 
4.9%
E 390
 
3.9%
A 263
 
2.6%
S 238
 
2.4%
G 157
 
1.6%
H 122
 
1.2%
B 106
 
1.1%
R 93
 
0.9%
Other values (10) 450
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 2231
74.0%
2 533
 
17.7%
3 143
 
4.7%
4 65
 
2.2%
5 19
 
0.6%
6 8
 
0.3%
7 6
 
0.2%
0 4
 
0.1%
8 3
 
0.1%
9 2
 
0.1%
Space Separator
ValueCountFrequency (%)
3399
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2998
100.0%
Other Punctuation
ValueCountFrequency (%)
% 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 65552
87.4%
Common 9413
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 11426
17.4%
s 6558
10.0%
n 6492
9.9%
e 6359
9.7%
d 5175
7.9%
h 4765
7.3%
F 4626
7.1%
a 3851
 
5.9%
p 3263
 
5.0%
L 2999
 
4.6%
Other values (35) 10038
15.3%
Common
ValueCountFrequency (%)
3399
36.1%
+ 2998
31.8%
1 2231
23.7%
2 533
 
5.7%
3 143
 
1.5%
4 65
 
0.7%
5 19
 
0.2%
6 8
 
0.1%
7 6
 
0.1%
0 4
 
< 0.1%
Other values (3) 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 11426
15.2%
s 6558
 
8.7%
n 6492
 
8.7%
e 6359
 
8.5%
d 5175
 
6.9%
h 4765
 
6.4%
F 4626
 
6.2%
a 3851
 
5.1%
3399
 
4.5%
p 3263
 
4.4%
Other values (48) 19051
25.4%

Time
Text

Distinct4510
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Memory size512.9 KiB
2024-08-12T21:03:13.892966image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length11
Median length1
Mean length4.147687
Min length1

Characters and Unicode

Total characters40975
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4433 ?
Unique (%)44.9%

Sample

1st row1:34:01.987
2nd row+11.415
3rd row+20.009
4th row+44.447
5th row+45.165
ValueCountFrequency (%)
0 5293
53.6%
17.456 3
 
< 0.1%
49.376 2
 
< 0.1%
24.720 2
 
< 0.1%
37.311 2
 
< 0.1%
16.543 2
 
< 0.1%
9.452 2
 
< 0.1%
7.756 2
 
< 0.1%
13.842 2
 
< 0.1%
23.604 2
 
< 0.1%
Other values (4500) 4567
46.2%
2024-08-12T21:03:14.210728image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7631
18.6%
. 4586
11.2%
1 4478
10.9%
+ 4117
10.0%
2 2790
 
6.8%
3 2763
 
6.7%
4 2475
 
6.0%
5 2453
 
6.0%
: 2145
 
5.2%
9 1921
 
4.7%
Other values (3) 5616
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30127
73.5%
Other Punctuation 6731
 
16.4%
Math Symbol 4117
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7631
25.3%
1 4478
14.9%
2 2790
 
9.3%
3 2763
 
9.2%
4 2475
 
8.2%
5 2453
 
8.1%
9 1921
 
6.4%
6 1911
 
6.3%
8 1861
 
6.2%
7 1844
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 4586
68.1%
: 2145
31.9%
Math Symbol
ValueCountFrequency (%)
+ 4117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40975
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7631
18.6%
. 4586
11.2%
1 4478
10.9%
+ 4117
10.0%
2 2790
 
6.8%
3 2763
 
6.7%
4 2475
 
6.0%
5 2453
 
6.0%
: 2145
 
5.2%
9 1921
 
4.7%
Other values (3) 5616
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7631
18.6%
. 4586
11.2%
1 4478
10.9%
+ 4117
10.0%
2 2790
 
6.8%
3 2763
 
6.7%
4 2475
 
6.0%
5 2453
 
6.0%
: 2145
 
5.2%
9 1921
 
4.7%
Other values (3) 5616
13.7%
Distinct7334
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Memory size537.6 KiB
2024-08-12T21:03:14.450088image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length6.7111044
Min length1

Characters and Unicode

Total characters66299
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6667 ?
Unique (%)67.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 1819
 
18.4%
1:17.495 4
 
< 0.1%
1:18.262 4
 
< 0.1%
1:43.026 4
 
< 0.1%
1:18.904 4
 
< 0.1%
1:35.816 3
 
< 0.1%
1:47.736 3
 
< 0.1%
1:14.934 3
 
< 0.1%
1:32.862 3
 
< 0.1%
1:29.230 3
 
< 0.1%
Other values (7324) 8029
81.3%
2024-08-12T21:03:14.810127image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12787
19.3%
: 8060
12.2%
. 8060
12.2%
2 5352
8.1%
3 5318
8.0%
0 5288
8.0%
4 4583
 
6.9%
5 3667
 
5.5%
7 3327
 
5.0%
6 3311
 
5.0%
Other values (2) 6546
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50179
75.7%
Other Punctuation 16120
 
24.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12787
25.5%
2 5352
10.7%
3 5318
10.6%
0 5288
10.5%
4 4583
 
9.1%
5 3667
 
7.3%
7 3327
 
6.6%
6 3311
 
6.6%
8 3286
 
6.5%
9 3260
 
6.5%
Other Punctuation
ValueCountFrequency (%)
: 8060
50.0%
. 8060
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66299
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12787
19.3%
: 8060
12.2%
. 8060
12.2%
2 5352
8.1%
3 5318
8.0%
0 5288
8.0%
4 4583
 
6.9%
5 3667
 
5.5%
7 3327
 
5.0%
6 3311
 
5.0%
Other values (2) 6546
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12787
19.3%
: 8060
12.2%
. 8060
12.2%
2 5352
8.1%
3 5318
8.0%
0 5288
8.0%
4 4583
 
6.9%
5 3667
 
5.5%
7 3327
 
5.0%
6 3311
 
5.0%
Other values (2) 6546
9.9%

FastestLapLap
Real number (ℝ)

ZEROS 

Distinct81
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.770625
Minimum0
Maximum85
Zeros1819
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:14.942771image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median41
Q352
95-th percentile66
Maximum85
Range85
Interquartile range (IQR)38

Descriptive statistics

Standard deviation22.333957
Coefficient of variation (CV)0.64232258
Kurtosis-1.1451737
Mean34.770625
Median Absolute Deviation (MAD)15
Skewness-0.3653994
Sum343499
Variance498.80565
MonotonicityNot monotonic
2024-08-12T21:03:15.068806image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1819
 
18.4%
50 305
 
3.1%
52 278
 
2.8%
53 274
 
2.8%
51 267
 
2.7%
48 223
 
2.3%
44 218
 
2.2%
49 217
 
2.2%
55 214
 
2.2%
43 211
 
2.1%
Other values (71) 5853
59.2%
ValueCountFrequency (%)
0 1819
18.4%
1 10
 
0.1%
2 56
 
0.6%
3 33
 
0.3%
4 55
 
0.6%
5 41
 
0.4%
6 54
 
0.5%
7 41
 
0.4%
8 42
 
0.4%
9 49
 
0.5%
ValueCountFrequency (%)
85 2
 
< 0.1%
80 3
 
< 0.1%
78 6
 
0.1%
77 12
0.1%
76 13
0.1%
75 16
0.2%
74 21
0.2%
73 5
 
0.1%
72 15
0.2%
71 27
0.3%

AverageSpeed
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7551
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.41417
Minimum0
Maximum257.32
Zeros1819
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:15.196120image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1169.427
median200.202
Q3213.935
95-th percentile235.2158
Maximum257.32
Range257.32
Interquartile range (IQR)44.508

Descriptive statistics

Standard deviation81.383176
Coefficient of variation (CV)0.48903994
Kurtosis0.32863497
Mean166.41417
Median Absolute Deviation (MAD)16.251
Skewness-1.4254981
Sum1644005.6
Variance6623.2214
MonotonicityNot monotonic
2024-08-12T21:03:15.329392image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1819
 
18.4%
207.069 4
 
< 0.1%
204.946 3
 
< 0.1%
200.363 3
 
< 0.1%
206.625 3
 
< 0.1%
229.633 3
 
< 0.1%
208.575 3
 
< 0.1%
213.224 3
 
< 0.1%
201.33 3
 
< 0.1%
222.592 3
 
< 0.1%
Other values (7541) 8032
81.3%
ValueCountFrequency (%)
0 1819
18.4%
89.54 1
 
< 0.1%
91.61 1
 
< 0.1%
100.615 1
 
< 0.1%
101.399 1
 
< 0.1%
101.884 1
 
< 0.1%
108.41 1
 
< 0.1%
112.116 1
 
< 0.1%
117.753 1
 
< 0.1%
118.872 1
 
< 0.1%
ValueCountFrequency (%)
257.32 1
< 0.1%
256.324 1
< 0.1%
255.874 1
< 0.1%
255.014 1
< 0.1%
254.861 1
< 0.1%
253.874 1
< 0.1%
253.566 1
< 0.1%
252.794 1
< 0.1%
252.77 1
< 0.1%
252.604 1
< 0.1%

Time_seconds
Real number (ℝ)

ZEROS 

Distinct4036
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.090854
Minimum0
Maximum207.071
Zeros5761
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:15.460562image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q332.3675
95-th percentile79.2015
Maximum207.071
Range207.071
Interquartile range (IQR)32.3675

Descriptive statistics

Standard deviation27.977976
Coefficient of variation (CV)1.546526
Kurtosis0.9562133
Mean18.090854
Median Absolute Deviation (MAD)0
Skewness1.4194128
Sum178719.55
Variance782.76715
MonotonicityNot monotonic
2024-08-12T21:03:15.586398image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5761
58.3%
17.456 3
 
< 0.1%
8.8 3
 
< 0.1%
71.529 2
 
< 0.1%
31.491 2
 
< 0.1%
49.376 2
 
< 0.1%
58.123 2
 
< 0.1%
78.14 2
 
< 0.1%
13.842 2
 
< 0.1%
35.759 2
 
< 0.1%
Other values (4026) 4098
41.5%
ValueCountFrequency (%)
0 5761
58.3%
0.011 1
 
< 0.1%
0.174 1
 
< 0.1%
0.179 1
 
< 0.1%
0.182 1
 
< 0.1%
0.215 1
 
< 0.1%
0.255 1
 
< 0.1%
0.293 1
 
< 0.1%
0.294 1
 
< 0.1%
0.415 1
 
< 0.1%
ValueCountFrequency (%)
207.071 1
< 0.1%
163.925 1
< 0.1%
162.163 1
< 0.1%
132.925 1
< 0.1%
132.791 1
< 0.1%
127.638 1
< 0.1%
122.416 1
< 0.1%
119.952 1
< 0.1%
116.119 1
< 0.1%
115.948 1
< 0.1%

FastestLapTime_seconds
Real number (ℝ)

ZEROS 

Distinct7334
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.129906
Minimum0
Maximum202.3
Zeros1819
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-08-12T21:03:15.713804image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q175.144
median85.996
Q397.534
95-th percentile110.44
Maximum202.3
Range202.3
Interquartile range (IQR)22.39

Descriptive statistics

Standard deviation36.95403
Coefficient of variation (CV)0.49850367
Kurtosis0.20837568
Mean74.129906
Median Absolute Deviation (MAD)11.253
Skewness-1.2695439
Sum732329.34
Variance1365.6003
MonotonicityNot monotonic
2024-08-12T21:03:15.853451image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1819
 
18.4%
78.904 4
 
< 0.1%
77.495 4
 
< 0.1%
78.262 4
 
< 0.1%
103.026 4
 
< 0.1%
99.199 3
 
< 0.1%
92.21 3
 
< 0.1%
78.069 3
 
< 0.1%
102.66 3
 
< 0.1%
81.134 3
 
< 0.1%
Other values (7324) 8029
81.3%
ValueCountFrequency (%)
0 1819
18.4%
55.404 1
 
< 0.1%
56.563 1
 
< 0.1%
56.789 1
 
< 0.1%
56.887 1
 
< 0.1%
56.905 1
 
< 0.1%
56.979 1
 
< 0.1%
57.001 1
 
< 0.1%
57.056 1
 
< 0.1%
57.165 1
 
< 0.1%
ValueCountFrequency (%)
202.3 1
< 0.1%
181.9 1
< 0.1%
176.181 1
< 0.1%
170.95 1
< 0.1%
168.804 1
< 0.1%
161.378 1
< 0.1%
159.612 1
< 0.1%
151.939 1
< 0.1%
147.276 1
< 0.1%
145.798 1
< 0.1%

Interactions

2024-08-12T21:03:06.728548image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
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2024-08-12T21:03:01.187577image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:02.128746image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:03.063666image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.003268image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.936829image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.890296image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:06.913108image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:56.677325image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:57.961939image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:58.922538image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:00.128977image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:01.270808image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:02.209535image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:03.144450image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.083055image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.019608image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.969517image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:07.003752image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:56.776062image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:58.046687image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:59.004320image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:00.213750image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:01.358014image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:02.292583image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:03.234970image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.168127image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.104961image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:06.053298image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:07.094509image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:56.875798image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:58.140436image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:59.086898image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:00.332451image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:01.443784image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:02.374868image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:03.327549image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.252900image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.187739image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:06.137126image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:07.187261image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:56.961568image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:58.223220image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:59.175184image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:00.417234image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:01.532070image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:02.457658image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:03.408334image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.349196image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.273509image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:06.222897image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:07.278019image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:57.049356image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:58.305001image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:59.262373image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:00.501008image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:01.617293image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:02.540931image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:03.490613image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.432395image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.358283image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:06.305675image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:07.367779image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:57.131640image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:58.384097image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:59.343654image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:00.583787image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:01.699075image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:02.623837image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:03.570399image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.510695image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.448462image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:06.387461image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:07.454546image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:57.214428image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:58.462097image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:59.425606image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:00.775782image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:01.779896image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:02.706615image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:03.652674image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.587794image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.533236image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:06.469242image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:07.545304image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:57.300199image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:58.544388image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:59.758721image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:00.917186image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:01.866007image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:02.793385image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:03.736958image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.672504image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.621999image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:06.553018image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:07.632472image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:57.388960image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:58.624176image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:59.846724image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:01.000962image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:01.947788image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:02.877165image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:03.821731image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:04.755804image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:05.706772image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:03:06.632805image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Correlations

2024-08-12T21:03:15.957414image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
AverageSpeedCircuitIDConstructorNameConstructorNationalityFastestLapLapFastestLapTime_secondsGridLapsNationalityPermanentNumberPointsPositionRoundSeasonTime_seconds
AverageSpeed1.0000.5110.2180.1160.4330.359-0.104-0.0120.1010.2620.214-0.2000.0650.4170.217
CircuitID0.5111.0000.0580.0540.2510.4000.0000.4080.0000.0820.0310.0000.6560.2790.096
ConstructorName0.2180.0581.0000.9990.1800.2080.3220.0850.3760.4060.2460.2280.0210.4350.100
ConstructorNationality0.1160.0540.9991.0000.0980.1120.2510.0500.3900.2900.1960.1720.0000.2510.068
FastestLapLap0.4330.2510.1800.0981.0000.264-0.0390.4950.0890.3040.276-0.2650.0930.4680.245
FastestLapTime_seconds0.3590.4000.2080.1120.2641.0000.048-0.2660.0930.2400.084-0.0140.0940.3570.203
Grid-0.1040.0000.3220.251-0.0390.0481.000-0.1300.185-0.149-0.5980.561-0.003-0.059-0.248
Laps-0.0120.4080.0850.0500.495-0.266-0.1301.0000.0400.0540.326-0.494-0.0080.0520.213
Nationality0.1010.0000.3760.3900.0890.0930.1850.0401.0000.5630.1530.1320.0000.2770.052
PermanentNumber0.2620.0820.4060.2900.3040.240-0.1490.0540.5631.0000.211-0.1170.0650.6360.138
Points0.2140.0310.2460.1960.2760.084-0.5980.3260.1530.2111.000-0.8770.0220.1840.457
Position-0.2000.0000.2280.172-0.265-0.0140.561-0.4940.132-0.117-0.8771.000-0.006-0.038-0.495
Round0.0650.6560.0210.0000.0930.094-0.003-0.0080.0000.0650.022-0.0061.0000.0930.052
Season0.4170.2790.4350.2510.4680.357-0.0590.0520.2770.6360.184-0.0380.0931.0000.162
Time_seconds0.2170.0960.1000.0680.2450.203-0.2480.2130.0520.1380.457-0.4950.0520.1621.000

Missing values

2024-08-12T21:03:07.804624image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T21:03:08.101046image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SeasonRoundCircuitIDPositionPointsDriverIDCodePermanentNumberGivenNameFamilyNameDateOfBirthNationalityConstructorNameConstructorNationalityGridLapsStatusTimeFastestLapTimeFastestLapLapAverageSpeedTime_secondsFastestLapTime_seconds
020001albert_park110.0michael_schumacherMSC0MichaelSchumacher1969-01-03GermanFerrariItalian358Finished1:34:01.987000.00.0000.0
120001albert_park26.0barrichelloBAR0RubensBarrichello1972-05-23BrazilianFerrariItalian458Finished+11.415000.011.4150.0
220001albert_park34.0ralf_schumacherSCH0RalfSchumacher1975-06-30GermanWilliamsBritish1158Finished+20.009000.020.0090.0
320001albert_park43.0villeneuveVIL0JacquesVilleneuve1971-04-09CanadianBARBritish858Finished+44.447000.044.4470.0
420001albert_park52.0fisichellaFIS0GiancarloFisichella1973-01-14ItalianBenettonItalian958Finished+45.165000.045.1650.0
520001albert_park61.0zontaZON0RicardoZonta1976-03-23BrazilianBARBritish1658Finished+46.468000.046.4680.0
620001albert_park70.0wurzWUR0AlexanderWurz1974-02-15AustrianBenettonItalian1458Finished+46.915000.046.9150.0
720001albert_park80.0geneNaN0MarcGené1974-03-29SpanishMinardiItalian1857+1 Lap0000.00.0000.0
820001albert_park90.0heidfeldHEI0NickHeidfeld1977-05-10GermanProstFrench1556+2 Laps0000.00.0000.0
920001albert_park100.0saloNaN0MikaSalo1966-11-30FinnishSauberSwiss1058Disqualified0000.00.0000.0
SeasonRoundCircuitIDPositionPointsDriverIDCodePermanentNumberGivenNameFamilyNameDateOfBirthNationalityConstructorNameConstructorNationalityGridLapsStatusTimeFastestLapTimeFastestLapLapAverageSpeedTime_secondsFastestLapTime_seconds
9869202414spa110.0strollSTR18LanceStroll1998-10-29CanadianAston MartinBritish1544Finished+1:03.0111:47.43537234.69463.011107.435
9870202414spa120.0albonALB23AlexanderAlbon1996-03-23ThaiWilliamsBritish1044Finished+1:03.6511:48.10544233.23963.651108.105
9871202414spa130.0gaslyGAS10PierreGasly1996-02-07FrenchAlpine F1 TeamFrench1244Finished+1:04.3651:47.99644233.47564.365107.996
9872202414spa140.0kevin_magnussenMAG20KevinMagnussen1992-10-05DanishHaas F1 TeamAmerican1744Finished+1:06.6311:47.41830234.73166.631107.418
9873202414spa150.0bottasBOT77ValtteriBottas1989-08-28FinnishSauberSwiss1444Finished+1:10.6381:47.84844233.79570.638107.848
9874202414spa160.0tsunodaTSU22YukiTsunoda2000-05-11JapaneseRB F1 TeamItalian2044Finished+1:16.7371:47.01937235.60676.737107.019
9875202414spa170.0sargeantSAR2LoganSargeant2000-12-31AmericanWilliamsBritish1844Finished+1:26.0571:47.96944233.53386.057107.969
9876202414spa180.0hulkenbergHUL27NicoHülkenberg1987-08-19GermanHaas F1 TeamAmerican1644Finished+1:28.8331:47.49043234.57488.833107.490
9877202414spa190.0zhouZHO24GuanyuZhou1999-05-30ChineseSauberSwiss195Retired01:48.95444231.4220.000108.954
9878202414spa200.0russellRUS63GeorgeRussell1998-02-15BritishMercedesGerman644Disqualified1:19:57.0401:52.0992224.9290.000112.099